Articles | Volume 23, issue 4
https://doi.org/10.5194/acp-23-2421-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-2421-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Feb 2023
Research article |
| 22 Feb 2023
| 22 Feb 2023
The impacts of dust aerosol and convective available potential energy on precipitation vertical structure in southeastern China as seen from multisource observations
Hongxia Zhu
School of Earth and Space Science, Comparative Planetary Excellence
Innovation Center, University of Science and Technology of China, Hefei
230026, China
Rui Li
CORRESPONDING AUTHOR
School of Earth and Space Science, Comparative Planetary Excellence
Innovation Center, University of Science and Technology of China, Hefei
230026, China
State Key Laboratory of Fire Science, University of Science and
Technology of China, Hefei 230026, China
Institut de recherche sur les forêts, Université du Québec en Abitibi-Témiscamingue (UQAT), Rouyn-Noranda, J9X 5E4, Canada
Shuping Yang
School of Earth and Space Science, Comparative Planetary Excellence
Innovation Center, University of Science and Technology of China, Hefei
230026, China
Chun Zhao
School of Earth and Space Science, Comparative Planetary Excellence
Innovation Center, University of Science and Technology of China, Hefei
230026, China
Zhe Jiang
School of Earth and Space Science, Comparative Planetary Excellence
Innovation Center, University of Science and Technology of China, Hefei
230026, China
Chen Huang
School of Earth and Space Science, Comparative Planetary Excellence
Innovation Center, University of Science and Technology of China, Hefei
230026, China
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Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
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Tai-Long He, Dylan B. A. Jones, Kazuyuki Miyazaki, Kevin W. Bowman, Zhe Jiang, Xiaokang Chen, Rui Li, Yuxiang Zhang, and Kunna Li
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Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng
Geosci. Model Dev., 15, 6143–6164, https://doi.org/10.5194/gmd-15-6143-2022, https://doi.org/10.5194/gmd-15-6143-2022, 2022
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Accurate prediction of aerosol pH in chemical transport models is essential to aerosol modeling. This study examines the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) on aerosol pH predictions and the sensitivities to emissions of nonvolatile cations and NH3, aerosol-phase state assumption, and heterogeneous sulfate production. Temporal evolution of aerosol pH during haze cycles in Beijing and the driving factors are also presented and discussed.
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Atmos. Chem. Phys., 22, 7815–7826, https://doi.org/10.5194/acp-22-7815-2022, https://doi.org/10.5194/acp-22-7815-2022, 2022
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We provide a comparative analysis to explore the effects of satellite and surface measurements on atmospheric CO in data assimilations in 2015–2020 over East Asia. We find possible overestimated enhancements of atmospheric CO by assimilating surface CO measurements due to model representation errors, and a large discrepancy in the derived trends of CO columns due to different vertical sensitivities of satellite and surface observations to lower and free troposphere.
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Geosci. Model Dev., 15, 4225–4237, https://doi.org/10.5194/gmd-15-4225-2022, https://doi.org/10.5194/gmd-15-4225-2022, 2022
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We present an application of a hybrid deep learning (DL) model on prediction of surface CO in China from 2015 to 2020, which utilizes both convolutional neural networks and long short-term memory neural networks. The DL model performance is better than a Kalman filter (KF) system in the training period (2005–2018). Furthermore, the DL model demonstrates good temporal extensibility: the mean bias and correlation coefficients are 95.7 ppb and 0.93 in the test period (2019–2020) over eastern China.
Xiaodong Wang, Chun Zhao, Mingyue Xu, Qiuyan Du, Jianqiu Zheng, Yun Bi, Shengfu Lin, and Yali Luo
Geosci. Model Dev., 15, 199–218, https://doi.org/10.5194/gmd-15-199-2022, https://doi.org/10.5194/gmd-15-199-2022, 2022
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Regional models are widely used to investigate aerosol climatic impacts. However, there are few studies examining the sensitivities of modeling results to regional domain size. In this study, the regional model is used to study the aerosol impacts on the East Asian summer monsoon system and focus on the modeling sensitivities to domain size. This study highlights the important impacts of domain size on regional modeling results of aerosol climatic impacts, which may not be limited to East Asia.
Yuqiang Zhang, Drew Shindell, Karl Seltzer, Lu Shen, Jean-Francois Lamarque, Qiang Zhang, Bo Zheng, Jia Xing, Zhe Jiang, and Lei Zhang
Atmos. Chem. Phys., 21, 16051–16065, https://doi.org/10.5194/acp-21-16051-2021, https://doi.org/10.5194/acp-21-16051-2021, 2021
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In this study, we use a global chemical transport model to simulate the effects on global air quality and human health due to emission changes in China from 2010 to 2017. By performing sensitivity analysis, we found that the air pollution control policies not only decrease the air pollutant concentration but also bring significant co-benefits in air quality to downwind regions. The benefits for the improved air pollution are dominated by PM2.5.
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Short summary
The impacts of atmospheric dust aerosols and cloud dynamic conditions on precipitation vertical development in southeastern China were studied using multiple satellite observations. It was found that the precipitating drops under dusty conditions grow faster in the middle layer but slower in the upper and lower layers compared with their pristine counterparts. Quantitative estimation of the sensitivity of the precipitation top temperature to the dust aerosol optical depth is also provided.
The impacts of atmospheric dust aerosols and cloud dynamic conditions on precipitation vertical...
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